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Missing observation analysis for matrix-variate time series data

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  • Triantafyllopoulos, K.

Abstract

Bayesian inference is developed for matrix-variate dynamic linear models (MV-DLMs), in order to allow missing observation analysis, of any sub-vector or sub-matrix of the observation time series matrix. We propose modifications of the inverted Wishart and matrix t distributions, replacing the scalar degrees of freedom by a diagonal matrix of degrees of freedom. The MV-DLM is then re-defined and modifications of the updating algorithm for missing observations are suggested.

Suggested Citation

  • Triantafyllopoulos, K., 2008. "Missing observation analysis for matrix-variate time series data," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2647-2653, November.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:16:p:2647-2653
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    References listed on IDEAS

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    1. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    2. Salvador, Manuel & Gargallo, Pilar, 2004. "Automatic monitoring and intervention in multivariate dynamic linear models," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 401-431, October.
    3. Paul H. Garthwaite & Shafeeqah A. Al‐Awadhi, 2001. "Non‐conjugate prior distribution assessment for multivariate normal sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 95-110.
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    Cited by:

    1. Sotiris Bersimis & Kostas Triantafyllopoulos, 2020. "Dynamic Non-parametric Monitoring of Air-Pollution," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1457-1479, December.
    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.

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